Breast ultrasound (BUS) image segmentation plays a crucial role in computer-aided diagnosis systems for BUS examination, which are useful for improved accuracy of breast cancer diagnosis. However, such performance remains a challenging task owing to the poor image quality and large variations in the sizes, shapes, and locations of breast lesions. In this paper, we propose a new convolutional neural network with coarse-to-fine feature fusion to address the aforementioned challenges.

中文翻译：

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乳腺超声图像分割：一种由粗到细融合的卷积神经网络

乳房超声 (BUS) 图像分割在用于 BUS 检查的计算机辅助诊断系统中起着至关重要的作用，有助于提高乳腺癌诊断的准确性。然而，由于图像质量差以及乳房病变的大小、形状和位置变化很大，这种性能仍​​然是一项具有挑战性的任务。在本文中，我们提出了一种新的具有粗到细特征融合的卷积神经网络来解决上述挑战。